Visualizing the structure of RNA-seq expression data using Grade of Membership Models

2019-05-02

Abstract

Grade of membership or GoM models (also known as admixture models or “Latent Dirichlet Allocation”) are a generalization of cluster models that allow each sample to have membership in multiple clusters. It is widely used to model ancestry of individuals in population genetics based on SNP microsatellite data and also in natural language processing for modeling documents [(???); Pritchard2000]. This R package implements tools to visualize the clusters obtained from fitting topic models using a Structure plot (Rosenberg et al. 2002) and extract the top features/genes that distinguish the clusters. In presence of known technical or batch effects, the package also allows for correction of these confounding effects.

1 Introduction

In the context of RNA-seq expression (bulk or singlecell seq) data, the grade of membership model allows each sample (usually a tissue sample or a single cell) to have some proportion of its RNA-seq reads coming from each cluster. For typical bulk RNA-seq experiments this assumption
can be argued as follows: each tissue sample is a mixture of different cell types, and so clusters could represent cell types (which are determined by the expression patterns of the genes), and the membership of a sample in each cluster could represent the proportions of each cell type present in that sample.

Many software packages available for document clustering are applicable to modeling RNA-seq data. Here, we use the R package maptpx(Taddy - International Conference on Artificial Intelligence and and 2012 2012) to fit these models, and add functionality for visualizing the results and annotating clusters by their most distinctive genes to help biological interpretation. We also provide additional functionality to correct for batch effects and also compare the outputs from two different grade of membership model fits to the same set of samples but different in terms of feature description or model assumptions.

In the above plot, the samples in each batch have been sorted by the proportional memebership of the most representative cluster in that batch. One can also use order_sample=FALSE for the un-ordered version, which retains the order as in the data (see Supplementary analysis for example).

Now we perform the Structure plot visualization for k=4 for GTEx V6 data for Brain samples .

6 Cluster Annotations

We extract the top genes driving each cluster using the ExtractTopFeatures functionality of the CountClust package. We first perform the cluster annotations from the GoM model fit with $k=6` on the single cell RNA-seq data due to Deng et al.

7 Supplementary analysis

As an additional analysis, we apply the CountClust tools on another single-cell RNA-seq data from mouse spleen due to Jaitin et al 2014 (Jaitin et al. 2014). The data had technical effects in the form of amplification batch which the user may want to correct for.

It seems from the above Structure plot that amplification batch drives the clusters. To remove the effect of amplification batch, one can use. For this, we use the BatchCorrectedCounts() functionality of the package.

8 Acknowledgements

We would like to thank Deng et al and the GTEx Consortium for having making the data publicly available. We would like to thank Matt Taddy, Amos Tanay, Po Yuan Tung and Raman Shah for helpful discussions related to the project and the package.